Widespread communications applications like email, document sharing, and social networking allow more and more people to be connected. As users' contacts lists grow ever larger, it becomes more difficult to determine the most relevant people to receive a message or join in a conversation. Existing software applications may make people recommendations for recipients to include in a user-created conversation. However, such applications may only take into account basic signals, such as the first letters of a user-input name, or most frequently messaged contacts, etc.
It would be desirable to provide a system that learns user preferences for people to include in a message or conversation, based on the content and context of prior user communications. For example, when composing an email related to a specific business project, a user could be provided with recommendations of people who have previously communicated with the user regarding the business project. Similarly, when browsing posts or updates on a social network, a user could be provided with recommendations of people to add as contacts.
Accordingly, techniques are desired for utilizing prior user communications to identify people most relevant to certain contextual signals contained therein, and leverage such signals to generate people recommendations.